Yes, a so-called 'expert in valuing companies' who doesn't do any actual investing himself.

I don't know anything about the man or his success record, but as an academic he may not wish to be accused of conflict of interest by promoting companies where he has a personal financial stake.

Academics (pure academics) don't have a good track record predicting valuations/prices, period.

The people who actually have the ability or to predict valuations/prices (better than the market) are busy making millions, if not billions, of dollars, and they most certainly aren't going to tell you about their predictions/models. If they do, it's after they've already invested or shorted the asset.

This isn't quite true and you've pretty much identified the reason why. Academics are not investors and their objective isn't to profit but to publish their research.

Academics identify a pattern that generates superior risk-adjusted returns and then publish their findings - this then leads to the anomaly disappearing as investors trade away the alpha. [0] The track record of an academic can therefore only be meaningfully discussed in terms of how well their model performs in back-testing. Saying they don't have a good track record misses this point.

Perhaps there are successful academic investors who achieve alpha and don't publish their research, however they wouldn't show up in any meta analysis.

[0] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3054718

Why would someone publish money making information rather than make the money for themselves?

> Why would someone publish money making information rather than make the money for themselves?

Perhaps because they enjoy academia far more than money making?

Some people like Grigori Perelman [0] are just not interested in money or fame, or both.

0: https://en.wikipedia.org/wiki/Grigori_Perelman

I’m not aware of Perelman publishing a “a pattern that generates superior risk-adjusted returns".

That type of information is worth millions, and there are many investment firms offering very lucrative positions to those that can identify a pattern that generates superior risk-adjusted returns.

Outside of very rare outliers, it doesn’t seem reasonable to assume that information that will generate outsized returns will be publicly available.

Because if your chosen career is an academic that's what you do. You research, write and publish. You could probably make more money by selling your ideas in private industry but that's an entire career pivot, it's like a junkyard becoming a cheap used car lot. It's a lot of work and comes with risk. If you're tenured or close to it you should probably just publish. Sure you could try and sell your ideas on the side but that's not going to endear you to other people in academia.

Or he just correctly observes that neither is a particularly good investment period.

It's possible to express an opinion on the relative value of two stocks while remaining neutral on their correlated returns, by going long one/short the other in the appropriate ratio.

A fair valuation may not be the same as market valuation since you can always bid for a stock high if there's a greater fool to buy it off you. This is what bubbles are made of - and once the music stops valuations will return to the fair valuation derived from multiples of revenue or asset values.

Damodaran is “overvalued” and grossly undervalued the entire tech landscape over the past decade. I would not call him an expert in anything.

We've been in a bull market for the past decade. I wouldn't trust any record, good or bad, that's only been tested in one half of a cycle.

Cool window into an interesting epistemological model. Considering the null hypothesis: "Damodaran is no better at predicting the market than any other person.", what would be evidence that rejects it?

Presumably not being in concordance with the market is insufficient evidence based off this and sibling comments. I wonder if there is a duration where this will even work.

Does there exist any evidence that would lead one to fail to reject the null hypothesis? Or would this model always reject?

Certainly, our current conclusion must be that Damodaran has poor predictive power over the short-term.

Before I have seen the musings of a fund manager posted, predicting the next downturn. Apparently he’s been doing this since before the last downturn. His fund is barely above flat since inception in the early 2000s, even though the S&P has more than doubled. After pointing out all that, I still got some respondents who claimed that active funds are known to underperform during good times and outperform in bear markets. Perhaps it is true but I remember feeling skeptical.

I guess there is no evidence that will convince everyone that someone is a bad money manager or prognosticator. That’s why, despite my general discomfort with Nassim Taleb, some of what he said in Skin in the Game really seems true. People can believe what they want, but beliefs don’t matter much if they aren’t confirmed, and the only meaningful way to confirm or reject market beliefs is via investment results.

Suppose you have someone that says "amazon is overvalued, there's no way they are worth more than $400B" over and over again for years as Amazon goes from $600B to $900B. I'd still keep an open mind as long as the market in general was irrationally exuberant over that period. It's only once the market as whole returned to sanity and most stocks went down, but amazon continued to raise that I'd say that guy probably doesn't know what he's talking about.

"there's nothing sticky about any of their business ventures"

Their critical mass of 'drivers' and their brand are hugely sticky, and massive barriers to entry.

Once Lyft and Uber have made sure that they are the only players, prices will inch up as they de-facto collude together, just like supermarket chains or anything else.

If a new entrant in a city tries to challenge them, they'll price way below market value until that challenger is gone, and then bring prices back up.

Wins and losses will be on a city-by-city basis, so it's possible that some city, somewhere has a 3rd party that wins out. Like maybe in Texas, due to whatever framework some other entity 'won' and came 1st (i.e. 'in Texas we proudly use 'Whatadriver'!), then it's possible for 3rd parties to exist, but the same rules will apply.

This market will settle down, and both Lyft and Uber will cut all of their stupid projects, and then they'll settle into oligarchy type pricing and profits.

As far as valuation, that's difficult to tell, but I think they have very viable business models that are not going away.

Facebook and Twitter could dissapear as quickly as they came, but Uber/Lyft found a back door into a very ancient market and they have their claws in their now, so they are not going away. They'll be around. It's just a matter of valuation at this point.

Most of the ubers and lyfts I've been in had both apps running. If a third app comes in with decent driver incentives, and can convince google maps to show them, they'll be able to get passengers and drivers. And, as you said, they only need to do it city by city.

I will say, when i needed a ride in a more rural area near Seattle, I did go right to Uber, and that driver wasn't on Lyft (but was considering it), so there is some stickiness, but I don't think it's much.

I see your point, but there has been a zillion attempts to do the Lyft/Uber thing and tons of money has already tried. The pie is just to big to ignore. So if hasn't happened already, it's not likely going to happen.

So yes, maybe a 'serious competitor' could try in some city, and as you say maybe 'word would travel fast' and maybe it might be possible to get a critical mass of drivers ... but then Uber/Lyft will just undercut them in a war of attrition.

To your mini-case: maybe there was a few other 3rd parties in existence, but you possibly never even heard of them. And even if the drivers sign up ... without the demand well there's no game anyhow.

It's been commoditized, so the advantage now is scale, brand etc. which the incumbents have, and they have money.

That market is done for now, until something comes from a different angle: new tech, new regulation, public transport shift, self driving cars etc.

You can't fight a subsidy war with uber/lyft now while they're still in growth mode. Once investors start demanding profits, and the prices go up, that's the time to come in and spend your money driving people around. Especially if you hear about others doing the same in other cities, or when some of the big loans are coming due.

It's a commodity, brand doesn't matter. Scale across cities doesn't really help that much. Money helps, but public market investors don't like to see it being spent quarter after quarter.

If food deliveries are made using the same driver fleet as the transport business then it is actually essential to squeeze more revenue per driver hours. Having the fleet there only to serve transport is inefficient because driver time is fungible between the businesses.

Why do people compare Uber to Apple, Google or Facebook? Why do people talk about them as if they were a tech company?

It's a taxi company. They have a web site and a mobile application, but so does several retailers as well as pretty much every bank out there. And no one is accusing them of being tech companies, or expect their growth to mirror that of Google or Facebook.

If you just look at the business of Uber, it's clear their current business does not inspire confidence that they will ever be profitable, much less deserving of Google-level valuations.

Sure, they may be able to become successful, but that would be on the back of a different business model. But then you'd have to justify why Uber would be able to execute on this as-of-yet-unknown business model better than any competitor.

> It's a taxi company. They have a web site and a mobile application, but so does several retailers as well as pretty much every bank out there

Taxi companies own taxis, calling uber a taxi company is too simplistic.

Correct, they also hire thousands of engineers to make the little cars move on the map.

> loss-making ventures like Food Delivery

Uber Eats revenue was $1.5 billion in 2018, representing an increase of 149% from the $0.6 billion in revenue the food delivery service generated in 2017.

What does that have to do with whether or not it’s loss making?